Network Selection Algorithm Based on Improved Deep Q-Learning
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摘要: 在引入休眠机制的超密集异构无线网络中，针对网络动态性增强，导致切换性能下降的问题，该文提出一种基于改进深度Q学习的网络选择算法。首先，根据网络的动态性分析，构建深度Q学习选网模型；其次，将深度Q学习选网模型中线下训练模块的训练样本与权值，通过迁移学习，将其迁移到线上决策模块中；最后，利用迁移的训练样本及权值加速训练神经网络，得到最佳选网策略。实验结果表明，该文算法显著改善了因休眠机制导致的高动态性网络切换性能下降问题，同时降低了传统深度Q学习算法在线上选网过程中的时间复杂度。Abstract: In ultra dense heterogeneous wireless network with sleep mechanism, in view of the problem that the network dynamic is enhanced and the handoff performance is reduced, a network selection algorithm based on improved deep Q-learning is proposed. Firstly, according to the dynamic analysis of the network, a deep Q-learning network selection model is constructed; Secondly, the training samples and weights of the offline training module in deep Q-learning network selection model, which are transferred to the online network decision-making module through the transfer learning; Finally, the training samples and weights of transfer are used to accelerate the process of training neural network, and the optimal network selection strategy is obtained. Experimental results demonstrate that the proposed algorithm improves significantly the performance degradation of high dynamic network handoff caused by sleep mechanism and the time complexity of traditional deep Q-learning algorithm for online network selection.
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表 1 候选网络的参数值
网络 接收信号强度(dBm) 路径损失(dB) 噪声偏差(dBm) 吞吐量(kbps) 负载量(个) MBS1 –85 48 6 1100 68 MBS2 –70 51 9 900 52 SBS1 –78 47 8 2700 16 SBS2 –72 53 8 2600 23 SBS3 –86 50 7 2900 25 SBS4 –95 49 6 3100 20 AP1 –60 45 9 4800 12 AP2 –75 43 6 6400 8 AP3 –71 47 7 5500 10 
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